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Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning
Contrastive self-supervised learning (CSL) leverages unlabeled data to t...
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Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a r...
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Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation
Unsupervised domain adaptation challenges the problem of transferring kn...
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A Lane-Changing Prediction Method Based on Temporal Convolution Network
Lane-changing is an important driving behavior and unreasonable lane cha...
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Reliable Evaluations for Natural Language Inference based on a Unified Cross-dataset Benchmark
Recent studies show that crowd-sourced Natural Language Inference (NLI) ...
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Robust Finite Mixture Regression for Heterogeneous Targets
Finite Mixture Regression (FMR) refers to the mixture modeling scheme wh...
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Domain Agnostic Learning for Unbiased Authentication
Authentication is the task of confirming the matching relationship betwe...
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Hybrid Differentially Private Federated Learning on Vertically Partitioned Data
We present HDP-VFL, the first hybrid differentially private (DP) framewo...
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Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learning
Transfer learning has become a common practice for training deep learnin...
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Relation-Guided Representation Learning
Deep auto-encoders (DAEs) have achieved great success in learning data r...
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Combating Domain Shift with Self-Taught Labeling
We present a novel method to combat domain shift when adapting classific...
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Why is Attention Not So Attentive?
Attention-based methods have played an important role in model interpret...
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Adversarial Infidelity Learning for Model Interpretation
Model interpretation is essential in data mining and knowledge discovery...
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General-Purpose User Embeddings based on Mobile App Usage
In this paper, we report our recent practice at Tencent for user modelin...
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PANDA: Prototypical Unsupervised Domain Adaptation
Previous adversarial domain alignment methods for unsupervised domain ad...
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A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation
This work addresses the unsupervised domain adaptation problem, especial...
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Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) aims to leverage the knowledge lear...
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Additive Adversarial Learning for Unbiased Authentication
Authentication is a task aiming to confirm the truth between data instan...
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Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets
Natural Language Sentence Matching (NLSM) has gained substantial attenti...
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Model-Protected Multi-Task Learning
Multi-task learning (MTL) refers to the paradigm of learning multiple re...
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Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free Approach
Partial person re-identification (re-id) is a challenging problem, where...
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Robust Localized Multi-view Subspace Clustering
In multi-view clustering, different views may have different confidence ...
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Weakly- and Semi-Supervised Object Detection with Expectation-Maximization Algorithm
Object detection when provided image-level labels instead of instance-le...
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Self-Paced Learning: an Implicit Regularization Perspective
Self-paced learning (SPL) mimics the cognitive mechanism of humans and a...
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